In the next century, our health care system will attempt to manage chronic illness in the largest aging population ever known. Non-adherence to pharmacological therapy and to non- pharmacological therapy will prove very costly. Hypertension, present in more than 50 million Americans, increases the risk of cardiovascular disease and its assoicated morbidity and mortality. Thus it is critical that adherence to treatment of hypertension be increased. While medications are effective in certain patients, their adverse effects make compliance with treatment difficult to ensure. In addition, more and more persons are turning to alternative medicine to deal with their health problems. Biofeedback offers an alternative to medical treatment, having been shown to reduce both systolic and diastolic blood pressures and/or allow the reduction of antihypertensive medications in some patients, while having no adverse effects. Yet biofeedback therapy is time-intensive and technician-intensive. Therefore, it is critical to be able to predict which patients with essential hypertension are most likely to lower his/her blood pressure using these techniques. This research proposes to test three different means of predicting whether a hypertensive subject will or will not be successful in lowering his/her blood pressure using biofeedback. Specifically, the first set of predictive criteria to be tested is that proposed by Weaver and McGrady (1995). This model is derived from five variables: heart rate, finger temperature, forehead muscle tension, plasma renin response to furosemide, and mean arterial pressure response to furosemide. The second prediction model is based on the magnitude of circadian variations in blood pressure as measured by 24-hour ambulatory blood pressure monitoring. The third prediction model is based on locus of control of behavior. A total of 60 hypertensive subjects will be studied over a three-year period. The results of this study will enable those caring for hypertensive persons to recommend treatment (i.e., biofeedback) in an individualized way, thereby promoting adherence.